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08/09/2023
Learning of discrete models of variational PDEs from data
We show how to learn discrete field theories from observational data of ...
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02/16/2023
Learning discrete Lagrangians for variationalPDEs from data and detection of travelling waves
The article shows how to learn models of dynamical systems from data whi...
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01/19/2023
Hamiltonian Neural Networks with Automatic Symmetry Detection
Recently, Hamiltonian neural networks (HNN) have been introduced to inco...
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11/20/2022
Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery
By one of the most fundamental principles in physics, a dynamical system...
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01/11/2022
Backward error analysis for conjugate symplectic methods
The numerical solution of an ordinary differential equation can be inter...
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12/23/2021
Variational integration of learned dynamical systems
The principle of least action is one of the most fundamental physical pr...
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08/05/2021
Symplectic integration of learned Hamiltonian systems
Hamiltonian systems are differential equations which describe systems in...
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04/08/2021
Efficient time stepping for numerical integration using reinforcement learning
Many problems in science and engineering require the efficient numerical...
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06/25/2020